PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar

doi:10.1155/2014/468176

Cited by 14 publications

(6 citation statements)

References 32 publications

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“…The feature extraction based on Isomap was implemented for applications like video manifold [34], semi-supervised local multi-manifold computation [35] and electromechanical equipment fault prediction [36]. The feature extraction based on EM-PCA was implemented for applications like face recognition [37] and classification in BCI [38]. The feature extraction based on PLS-NLR is useful in applications like development of Relevance Feature vector machine [39] and denoising application [40].…”

Section: Feature Extractionmentioning

confidence: 99%

A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

Prabhakar

Rajaguru²,

Lee

2020

IEEE Access

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One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizophrenia from Electroencephalography (EEG) signals. As EEG can analyze a lot of brain disorders and is used to study the diseases of the brain in an in-depth manner, it can be used to analyze the schizophrenia EEG signals. In this paper, three feature extraction techniques are employed such as Partial Least Squares (PLS) Non linear Regression technique, Expectation Maximization based Principal Component Analysis (EM-PCA) technique and Isometric Mapping (Isomap) technique. The extracted features are further optimized with four optimization algorithms such as Flower Pollination algorithm, Eagle strategy using different evolution algorithm, Backtracking search optimization algorithm and Group search optimization algorithm. The optimized values are then classified with varied versions of both Adaboost classifier and Naïve Bayesian Classifier. The individual results show that for normal cases, Isomap features when optimized with Backtracking search optimization algorithm and classified with Modest Adaboost classifier, a classification accuracy of 98.77% is obtained. The individual results show that for schizophrenia case, when Isomap features are optimized with Flower Pollination optimization algorithm and classified with Real Adaboost classifier, a classification accuracy of 98.77% is obtained.

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Section: Feature Extractionmentioning

confidence: 99%

A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

Prabhakar

Rajaguru²,

Lee

2020

IEEE Access

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“…Thus, with the help of the TL model, we reduced one of the major limitations of DL, which is due to computational cost. We evaluated our model on the most widely and well‐accepted data sets consisting of facial biometric [37 ] and animal data (ASSIRA) [38 ] with optimal performance of classification accuracy of 98.27 and 93.45%, respectively.…”

Section: Performance Evaluation and Scientific Validationmentioning

confidence: 99%

Brain MRI‐based Wilson disease tissue classification: an optimised deep transfer learning approach

et al. 2020

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Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer‐aided design‐based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group‐19 (VGG‐19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four‐fold augmentation, VGG‐19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70 %, 0.932 (p < 0.0001 ) and 86.87 ± 2.23 %, 0.871 (p < 0.0001 ), respectively. Further, MobileNet and VGG‐19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML‐based soft classifier – Random Forest.

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“…PCA is widely used for face detection and recognition beacuse of achieving high accuracy while requiring a small number of features [9].…”

Section: Related Workmentioning

confidence: 99%

“…One approach is calculating the eigenvectors and eigenvalues without calculating the covariance matrix. Methods like the expectation maximization algorithm (EM) to reduce the determinant matrix manipulation [9].…”

Section: Related Workmentioning

confidence: 99%

Parallel Architecture for Face Recognition using MPI

Ibrahim

Hamdy

2017

ijacsa

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Abstract-The face recognition applications are widely used in different fields like security and computer vision. The recognition process should be done in real time to take fast decisions. Principle Component Analysis (PCA) considered as feature extraction technique and is widely used in facial recognition applications by projecting images in new face space. PCA can reduce the dimensionality of the image. However, PCA consumes a lot of processing time due to its high intensive computation nature. Hence, this paper proposes two different parallel architectures to accelerate training and testing phases of PCA algorithm by exploiting the benefits of distributed memory architecture. The experimental results show that the proposed architectures achieve linear speed-up and system scalability on different data sizes from the Facial Recognition Technology (FERET) database.

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PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Cited by 14 publications

References 32 publications

A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

Brain MRI‐based Wilson disease tissue classification: an optimised deep transfer learning approach

Parallel Architecture for Face Recognition using MPI

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